Download Digital Forensics and Watermaking: 11th International by Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi, PDF

By Yane Zhong, Fangjun Huang, Dong Zhang (auth.), Yun Q. Shi, Hyoung-Joong Kim, Fernando Pérez-González (eds.)

This publication constitutes the completely refereed post-proceedings of the eleventh foreign Workshop on Digital-Forensics and Watermarking, IWDW 2012, held in Shanghai, China, in the course of October/November 2012. The forty two revised papers (27 oral and 15 poster papers) have been conscientiously reviewed and chosen from 70 submissions. The papers are geared up in topical sections on steganography and steganalysis; watermarking and copyright safety; forensics and anti-forensics; reversible information hiding; fingerprinting and authentication; visible cryptography.

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Additional info for Digital Forensics and Watermaking: 11th International Workshop, IWDW 2012, Shanghai, China, October 31 – November 3, 2012, Revised Selected Papers

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Our proposed mapping technology may go as follows. We start with arranging all of five probabilities in a non-increasing order, and then renumber them as 0, 1, 2, 3, 4, as show in Eq. 5. The procedures go as follows. 47 It is noted that this mapping process does look like Huffman coding [19], in which one always first lists all items with their associated probabilities in a nonincreasing order, then aggregates the last two items associated with the two least probabilities into a new item, this process repeats until only two items remain.

Now the co-occurrence matrix generated by considering four horizontally consecutive elements of a residual image can be represented as follows. C h = |{(ri,j , ri,j+1 , ri,j+2 , ri,j+3 ) |ri,j , ri,j+1 , ri,j+2 , ri,j+3 ∈ [−T : T ]}| (2) where i and j are coordinates in vertical and horizontal two directions, respectively, and T is the threshold. Other three types of co-occurrence C v ,C d and C m are defined analogously. As in [13] the T is selected as T = 2 . Hence the total number of features generated from each residual image is (2T +1)4 = 625.

2 Proposed Mapping Technique In this section we first provide some background on residual image and cooccurrence matrix. Then, the proposed mapping technique is presented. 1 Background and Residual Image Co-occurrence matrix was proposed as textural features for image classification in 1973 [16]. Since then it has been widely utilized for image classification (as of 2012, having been cited about 7,000 times according to Google). In steganalysis, a special type of image classification, the co-occurrence matrix has been widely used as well [17,11,13].

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